# VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays

arXiv: https://arxiv.org/abs/2107.01327 · HTML: https://mlsanity.com/paper/2107.01327/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 3 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations](https://mlsanity.com/paper/2012.15029/) (2020)
- [Sequential Rib Labeling and Segmentation in Chest X-Ray using Mask R-CNN](https://mlsanity.com/paper/1908.08329/) (2019)

## Built on by (most cited first, 23 of 23)

- [Exploring scalable medical image encoders beyond text supervision](https://mlsanity.com/paper/2401.10815/) (2024)
- [Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning](https://mlsanity.com/paper/2405.01469/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis](https://mlsanity.com/paper/2203.10611/) (2022)
- [Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-Supervision](https://mlsanity.com/paper/2404.15672/) (2024)
- [Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervision](https://mlsanity.com/paper/2309.15358/) (2023)
- [Foundation Ark: Accruing and Reusing Knowledge for Superior and Robust Performance](https://mlsanity.com/paper/2310.09507/) (2023)
- [An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph](https://mlsanity.com/paper/2208.03545/) (2022)
- [Chest X-ray Foundation Model with Global and Local Representations Integration](https://mlsanity.com/paper/2502.05142/) (2025)
- [Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese](https://mlsanity.com/paper/2209.04794/) (2022)
- [Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray Analysis](https://mlsanity.com/paper/2503.09860/) (2025)
- [Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods](https://mlsanity.com/paper/2301.09338/) (2023)
- [An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis](https://mlsanity.com/paper/2302.09696/) (2023)
- [A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling](https://mlsanity.com/paper/2602.22843/) (2026)
- [Autonomous Computer Vision Development with Agentic AI](https://mlsanity.com/paper/2506.11140/) (2025)
- [Multi Anatomy X-Ray Foundation Model](https://mlsanity.com/paper/2509.12146/) (2025)
- [RadJEPA: Radiology Encoder for Chest X-Rays via Joint Embedding Predictive Architecture](https://mlsanity.com/paper/2601.15891/) (2026)
- [Semantic Alignment of Unimodal Medical Text and Vision Representations](https://mlsanity.com/paper/2503.04478/) (2025)
- [PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism](https://mlsanity.com/paper/2507.00673/) (2025)
- [ViX-Ray: A Vietnamese Chest X-Ray Dataset for Vision-Language Models](https://mlsanity.com/paper/2603.15513/) (2026)
- [RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection](https://mlsanity.com/paper/2605.17368/) (2026)
- [CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs](https://mlsanity.com/paper/2606.08420/) (2026)
- [FleXray: Universal Clinical X-ray Segmentation](https://mlsanity.com/paper/2609.26756/) (2026)

## Abstract (arXiv)

We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans. The VinDr-RibCXR contains 245 CXRs with corresponding ground truth annotations provided by human experts. A set of state-of-the-art segmentation models are trained on 196 images from the VinDr-RibCXR to segment and label 20 individual ribs. Our best performing model obtains a Dice score of 0.834 (95% CI, 0.810--0.853) on an independent test set of 49 images. Our study, therefore, serves as a proof of concept and baseline performance for future research.